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Objective: The correlation of depleted blood through midline shift in acute subdural hematoma remains the most reliable clinical predictor to date. On the other hand, patient’s ABO blood type has a profound impact on coagulation and hemostasis. We conducted this study to evaluate the role of patient’s blood type in terms of incidence, clinical course and outcome after acute subdural hematoma bleeding.
Methods: 100 patients with acute subdural hematoma treated between 2010 and 2015 at the author’s institution were included. Baseline characteristics and clinical findings including Glasgow coma scale, Glasgow outcome scale, hematoma volume, rebleeding, midline shift, postoperative seizures and the presence of anticoagulation were analyzed for their association with ABO blood type.
Results: Patient’s with blood type O were found to have a lower midline shift (p<0.01) and significantly less seizures (OR: 0.43; p<0.05) compared to non-O patients. Furthermore, patients with blood type A had the a significantly higher midline shift (p<0.05) and a significantly increased risk for postoperative seizures (OR: 4.01; p<0.001). There was no difference in ABO blood type distribution between acute subdural hematoma patients and the average population.
Conclusion: The ABO blood type has significant influence on acute subdural hematoma sequelae. Patient’s with blood type O benefit in their clinical course after acute subdural hematoma whereas blood type A patients are at highest risk for increased midline shift and postoperative seizures. Further studies elucidating the biological mechanisms of blood type depended hemostaseology and its role in acute subdural hematoma are required for the development of an appropriate intervention.
Replacement therapy in severe hemophilia A leads to factor VIII (FVIII) inhibitors in 30% of patients. Factor VIII gene (F8) mutation type, a family history of inhibitors, ethnicity and intensity of treatment are established risk factors, and were included in two published prediction tools based on regression models. Recently investigated immune regulatory genes could also play a part in immunogenicity. Our objective is to identify bio-clinical and genetic markers for FVIII inhibitor development, taking into account potential genetic high order interactions. The study population consisted of 593 and 79 patients with hemophilia A from centers in Bonn and Frankfurt respectively. Data was collected in the European ABIRISK tranSMART database. A subset of 125 severely affected patients from Bonn with reliable information on first treatment was selected as eligible for risk stratification using a hybrid tree-based regression model (GPLTR). In the eligible subset, 58 (46%) patients developed FVIII inhibitors. Among them, 49 (84%) were “high risk” F8 mutation type. 19 (33%) had a family history of inhibitors. The GPLTR model, taking into account F8 mutation risk, family history of inhibitors and product type, distinguishes two groups of patients: a high-risk group for immunogenicity, including patients with positive HLA-DRB1*15 and genotype G/A and A/A for IL-10 rs1800896, and a low-risk group of patients with negative HLA-DRB1*15 / HLA-DQB1*02 and T/T or G/T for CD86 rs2681401. We show associations between genetic factors and the occurrence of FVIII inhibitor development in severe hemophilia A patients taking into account for high-order interactions using a generalized partially linear tree-based approach.